Automate business workflows with multi-AI agent systems. Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language.

Use the AutoGen framework to build multi-agent systems with diverse roles and capabilities for implementing complex AI applications.

Learn a flexible framework to build a variety of complex AI applications.

Efficiently handle time-varying workloads with serverless agentic workflows and responsible agents built on Amazon Bedrock.

Build agents that collaborate to solve complex business tasks.

Learn how an AI Assistant is built to use and accomplish tasks on computers.

Learn how to systematically evaluate, improve, and iterate on AI agents using structured assessments.

Learn to build, debug, and deploy applications with an Agentic AI-powered integrated development environment.

Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback.

Build agents that navigate and interact with websites, and learn how to make them more reliable.

Build agents that write and execute code to perform complex tasks, using Hugging Face’s smolagents.

Build AI apps that access tools, data, and prompts using the Model Context Protocol.

Build, debug, and optimize AI agents using DSPy and MLflow.

Turn your GenAI prototype into an automated pipeline using Apache Airflow